arXiv subjects

Qi Yao

Publications and source records attributed to Qi Yao.

At least 19 recordsLinked to original sources

arXiv2026

Microscopic Theory of Drag in a Bose Condensate Interacting with a Moving Reservoir

We derive a microscopic theory of drag for a Bose-Einstein condensate interacting with a moving reservoir. Starting from interactions between a condensate and a drifting fermionic bath, we integrate out the bath degrees of freedom within the Born--Markov approximation to obtain an effective Gross--Pitaevskii equation with a drag potential. From this potential we derive an effective drag force and obtain a closed expression for the drag coefficient, determined by the reservoir density fluctuations and the condensate density profile. As an application, we simulate the drag for an exciton--polariton condensate interacting with a drifting electron gas.

cond-mat.quant-gas↗

arXiv2026

Extending Qubit Coherence Time via Hybrid Dynamical Decoupling

Dynamical decoupling (DD) and bath engineering are two parallel techniques employed to mitigate qubit decoherence resulting from their unavoidable coupling to the environment. Here, we present a hybrid DD approach that integrates pulsed DD with bath spin polarization to enhance qubit coherence within the central spin model. This model, which can be realized using GaAs semiconductor quantum dots or analogous quantum simulators, demonstrates a significant extension of the central spin's coherence time by approximately 2 to 3 orders of magnitude that compared with the free-induced decay time, where the dominant contribution from DD and a moderate improvement from spin-bath polarization. This study, which integrates uniaxial dynamical decoupling and auxiliary bath-spin engineering, paves the way for prolonging coherence times in various practical quantum systems, including GaAs/AlGaAs, silicon and Si/SiGe. And this advancement holds substantial promise for applications in quantum information processing.

quant-ph↗

arXiv2025

Replay Failures as Successes: Sample-Efficient Reinforcement Learning for Instruction Following

Reinforcement Learning (RL) has shown promise for aligning Large Language Models (LLMs) to follow instructions with various constraints. Despite the encouraging results, RL improvement inevitably relies on sampling successful, high-quality responses; however, the initial model often struggles to generate responses that satisfy all constraints due to its limited capabilities, yielding sparse or indistinguishable rewards that impede learning. In this work, we propose Hindsight instruction Replay (HiR), a novel sample-efficient RL framework for complex instruction following tasks, which employs a select-then-rewrite strategy to replay failed attempts as successes based on the constraints that have been satisfied in hindsight. We perform RL on these replayed samples as well as the original ones, theoretically framing the objective as dual-preference learning at both the instruction- and response-level to enable efficient optimization using only a binary reward signal. Extensive experiments demonstrate that the proposed HiR yields promising results across different instruction following tasks, while requiring less computational budget. Our code and dataset is available at https://github.com/sastpg/HIR.

cs.AI↗

arXiv2025

Asymptotic Behavior of Homogeneous Complex Monge-Ampere Equations on ALE Kahler manifolds

This paper is a sequel to the author's earlier work and investigates the homogeneous complex Monge--Ampere equation (HCMA) on the product space $X \times D$, where $X$ is an asymptotically locally Euclidean (ALE) Kahler manifold and $D subset C$ is the unit disc. We establish precise asymptotic behavior of the solution to the HCMA equation, showing that the decay rate of the solution matches that of the prescribed boundary data and that uniform control in weighted Holder norms can be achieved. The analysis combines two main ingredients: a redevelopment of pluripotential theory on the noncompact space $X \times D$ and a PDE-based construction of holomorphic disc foliations on the end of $X$, inspired by the works of Semmes and Donaldson. As an application in general Kahler manifolds, the techniques developed in this paper also imply a local regularity result for the HCMA equation.

math.AP↗

arXiv2025

Mirror-mediated long-range coupling and robust phase locking of spatially separated exciton-polariton condensates

Lattice arrays are valuable simulators for complex mathematical problems, but physical systems typically allow only short-range coupling. We demonstrate a method for independently tunable, long-range interactions between polariton condensates in two-dimensional lattices by using vertical emission and external imaging to couple arbitrary sites. Two geometrically isolated condensates are phase-locked without planar coupling, verified via phase-resolved interferometry. Analytical modeling reveals mechanisms for robust coherence. The mirror-based scheme, free of cameras or modulators, offers a pure, high-bandwidth analogt element. Extension to dense graphs via segmented micro-mirrors is limited only by imaging optics, enabling scalable, energy-efficient polaritonic hardware for neuromorphic computation.

physics.optics↗

arXiv2025

Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning

Recent advances of Reinforcement Learning (RL) have highlighted its potential in complex reasoning tasks, yet effective training often relies on external supervision, which limits the broader applicability. In this work, we propose a novel self-rewarding reinforcement learning framework to enhance Large Language Model (LLM) reasoning by leveraging the consistency of intermediate reasoning states across different reasoning trajectories. Our key insight is that correct responses often exhibit consistent trajectory patterns in terms of model likelihood: their intermediate reasoning states tend to converge toward their own final answers (high consistency) with minimal deviation toward other candidates (low volatility). Inspired by this observation, we introduce CoVo, an intrinsic reward mechanism that integrates Consistency and Volatility via a robust vector-space aggregation strategy, complemented by a curiosity bonus to promote diverse exploration. CoVo enables LLMs to perform RL in a self-rewarding manner, offering a scalable pathway for learning to reason without external supervision. Extensive experiments on diverse reasoning benchmarks show that CoVo achieves performance comparable to or even surpassing supervised RL. Our code is available at https://github.com/sastpg/CoVo.

cs.AI↗

arXiv2025

Shubnikov-de Haas quantum oscillations with large spin splitting in highmobility Al0.8Ga0.2Sb/InAs/ Al0.8Ga0.2Sb quantum-well heterostructures

We report the epitaxial growth of high-quality Al0.8Ga0.2Sb-InAs-Al0.8Ga0.2Sb quantum well films featured by high carrier mobility and strong spin-orbit coupling. By appropriately optimizing the Al-to-Ga ratio in the AlGaSb barrier layer, the quantum confinement of the heterostructure is significantlyenhanced, which results in both an ultra-high electron mobility of 924000 cm2/Vs and a giant magnetoresistance ratio of 365000 at low temperatures. Meanwhile, pronounced Shubnikov-deHaas quantum oscillations persist up to 30 K, and their single-frequency feature indicates a well defined Fermi surface without subband mixing in the two-dimensional electron gas channel. Moreover, the large effective g-factor of 12.93 leads to the observation of Zeeman splitting at large magnetic fields. Our results validate the AlGaSb/InAs quantum well heterostructures as a suitable candidate for constructing energy-efficient topological spintronic devices.

cond-mat.mes-hall↗

arXiv2025

GarmentDiffusion: 3D Garment Sewing Pattern Generation with Multimodal Diffusion Transformers

Garment sewing patterns are fundamental design elements that bridge the gap between design concepts and practical manufacturing. The generative modeling of sewing patterns is crucial for creating diversified garments. However, existing approaches are limited either by reliance on a single input modality or by suboptimal generation efficiency. In this work, we present GarmentDiffusion, a new generative model capable of producing centimeter-precise, vectorized 3D sewing patterns from multimodal inputs (text, image, and incomplete sewing pattern). Our method efficiently encodes 3D sewing pattern parameters into compact edge token representations, achieving a sequence length that is 10 times shorter than that of the autoregressive SewingGPT in DressCode. By employing a diffusion transformer, we simultaneously denoise all edge tokens along the temporal axis, while maintaining a constant number of denoising steps regardless of dataset-specific edge and panel statistics. With all combination of designs of our model, the sewing pattern generation speed is accelerated by 100 times compared to SewingGPT. We achieve new state-of-the-art results on DressCodeData, as well as on the largest sewing pattern dataset, namely GarmentCodeData. The project website is available at https://shenfu-research.github.io/Garment-Diffusion/.

cs.CV↗

arXiv2025

Reasoning with Reinforced Functional Token Tuning

In this work, we propose Reinforced Functional Token Tuning (RFTT), a novel reinforced fine-tuning framework that empowers Large Language Models (LLMs) with self-play learn-to-reason capabilities. Unlike prior prompt-driven reasoning efforts, RFTT embeds a rich set of learnable functional tokens (e.g., , , ) directly into the model vocabulary, enabling chain-of-thought construction with diverse human-like reasoning behaviors. Specifically, RFTT comprises two phases: (1) supervised fine-tuning performs prompt-driven tree search to obtain self-generated training data annotated with functional tokens, which warms up the model to learn these tokens for reasoning; and (2) online reinforcement learning further allows the model to explore different reasoning pathways through functional token sampling without relying on prompts, thereby facilitating effective self-improvement for functional reasoning. Extensive experiments demonstrate the superiority of the proposed RFTT on mathematical benchmarks, significantly boosting Qwen-2.5-7B-Instruct (70.6% to 79.8%) and LLaMA-3.1-8B-Instruct (32.2% to 60.2%) on the MATH dataset. Moreover, the performance of RFTT consistently improves with more search rollouts at inference time. Our code is available at https://github.com/sastpg/RFTT.

cs.AI↗

arXiv2024

Electrical detection in two-terminal perpendicularly magnetized devices via geometric anomalous Nernst effect

The non-uniform current distribution arisen from either current crowding effect or hot spot effect provides a method to tailor the interaction between thermal gradient and electron transport in magnetically ordered systems. Here we apply the device structural engineering to realize an in-plane inhomogeneous temperature distribution within the conduction channel, and the resulting geometric anomalous Nernst effect (GANE) gives rise to a non-zero 2nd -harmonic resistance whose polarity corresponds to the out-of-plane magnetization of Co/Pt multi-layer thin film, and its amplitude is linearly proportional to the applied current. By optimizing the aspect ratio of convex-shaped device, the effective temperature gradient can reach up to 0.3 K/$μ$m along the y-direction, leading to a GANE signal of 28.3 $μ$V. Moreover, we demonstrate electrical write and read operations in the perpendicularly-magnetized Co/Pt-based spin-orbit torque device with a simple two-terminal structure. Our results unveil a new pathway to utilize thermoelectric effects for constructing high-density magnetic memories

cond-mat.mes-hall↗

arXiv2024

Tunable interfacial Rashba spin-orbit coupling in asymmetric Al$_x$In$_{1-x}$Sb/InSb/CdTe quantum well heterostructures

The manipulation of Rashba-type spin-orbit coupling (SOC) in molecular beam epitaxy-grown Al$_x$In$_{1-x}$Sb/InSb/CdTe quantum well heterostructures is reported. The effective band bending provides robust two-dimensional quantum confinement, while the unidirectional built-in electric field from the asymmetric hetero-interfaces results in pronounced Rashba SOC strength. By tuning the Al concentration in the top Al$_x$In$_{1-x}$Sb barrier layer, the optimal structure with $x = 0.15$ shows the largest Rashba coefficient of 0.23 eV-Angstrom. and the highest low-temperature electron mobility of 4400 cm$^2$/Vs . Quantitative investigations of the weak anti-localization effect further confirm the dominant D'yakonov-Perel (DP) spin relaxation mechanism during charge-to-spin conversion. These findings highlight the significance of quantum well engineering in shaping magneto-resistance responses, and narrow bandgap semiconductor-based heterostructures may offer a reliable platform for energy-efficient spintronic applications.

cond-mat.mtrl-sci↗

arXiv2024

Wave functions in the Critical Phase: a Planar \textit{Sierpiński} Fractal Lattice

Electronic states play a crucial role in many quantum systems of moire superlattices, quasicrystals, and fractals. As recently reported in \textit{Sierpiński} lattices [Phys. Rev. B 107, 115424 (2023)], the critical states are revealed by the energy level-correlation spectra, which are caused by the interplay between aperiodicity and determined self-similarity characters. In the case of the \textit{Sierpiński Carpet}, our results further demonstrate that there is some degree of spatial overlap between these electronic states. These states could be strongly affected by its `seed lattice' of the $generator$, and slightly modulated by the dilation pattern and the geometrical self-similarity level. These electronic states are multifractal by scaling the $q$-order inverse participation ratio or fractal dimension, which correlates with the subdiffusion behavior. In the $gene$ pattern, the averaged state-based multifractal dimension of second-order would increase as its \textit{Hausdoff dimension} increases. Our findings could potentially contribute to understanding quantum transports and single-particle quantum dynamics in fractals.

cond-mat.mes-hall↗

arXiv2024

Bose-Einstein condensation of polaritons at room temperature in a GaAs/AlGaAs structure

We report the canonical properties of Bose-Einstein condensation of polaritons, seen previously in many low-temperature experiments, at room temperature in a GaAs/AlGaAs structure. These effects include a nonlinear energy shift of the polaritons, showing that they are not non-interacting photons, and dramatic line narrowing due to coherence, giving coherent emission with spectral width of 0.24 meV at room temperature with no external stabilization. This opens up the possibility of room temperature nonlinear optical devices based on polariton condensation.

cond-mat.quant-gas↗

arXiv2024

Measurement of exciton fraction of microcavity exciton-polaritons using transfer-matrix modeling

We present a careful calibration of the exciton fraction of polaritons in high-$Q$ ($\sim 300,000$), long-lifetime ($\sim 300$ ps), GaAs/AlGaAs microcavities.This is a crucial parameter for many-body theories which include the polariton-polariton interactions.It is much harder to establish this number in high-$Q$ structures compared to low-$Q$ structures, because the upper polariton is nearly invisible in high-$Q$ cavities.We present a combination of photoluminescence, photoluminescence excitation, and reflectivity measurements to highly constrain the fit model, and compare the results of this model to the results from low-$Q$ structures.We present a fitted curve of exciton fraction as a function of the lower polariton energy for multiple samples which have been used in prior experiments.

cond-mat.mes-hall↗

arXiv2024

Electronic states and quantum transport in bilayer graphene Sierpinski-carpet fractals

We construct Sierpinski-carpet (SC) based on AA or AB bilayer graphene by atom vacancies, namely, SC-AA and SC-AB, to investigate the effects of interlayer coupling on the electronic properties of fractals. Compared with monolayer graphene SC, their density of states have similar features, such as Van-Hove singularities and edge states corresponding to the central peaks near zero energy, but remarkable energy broadening of edge states emerges in SC-AA(AB). Calculated conductance spectrum shows that the conductance fluctuations still hold the Hausdorff fractal dimension behavior even with the interlayer coupling. Thus, the high correlation between quantum conductance and fractal geometry dimension is not affected by the interlayer coupling in bilayer graphene SC. We further reveal the quasi-eigenstates in fractal-like pressure-modulated bilayer graphene, namely, SC-pAA and SC-pAB. Numerical results show that the density of states of SC-pAA(pAB) show an asymptotic behavior to those of SC-AA(AB) especially for high energy quasi-eigenstates. Within a certain energy range, stronger pressure can lead to stronger localization, forming an efficient fractal space.

cond-mat.mes-hall↗

arXiv2024

Geodesic Equations on asymptotically locally Euclidean Kähler manifolds

We solve the geodesic equation in the space of Kähler metrics under the setting of asymptotically locally Euclidean (ALE) Kähler manifolds and we prove global $\mathcal{C}^{1,1}$ regularity of the solution. Then, we relate the solution of the geodesic equation to the uniqueness of scalar-flat ALE metrics. To this end, we study the asymptotic behavior of $\varepsilon$-geodesics at spatial infinity. Under the assumption that the Ricci curvature of a reference ALE Kähler metric is non-positive, convexity of the Mabuchi $K$-energy along $\varepsilon$-geodesics. However, we will also prove that on the line bundle $\mathcal{O}(-k)$ over $\mathbb{C}\mathbb{P}^{n-1}$ with $n \geq 2$ and $k \neq n$, no ALE Kähler metric can have non-positive (or non-negative) Ricci curvature.

math.DG↗

arXiv2023

Weakly Supervised Detection of Baby Cry

Detection of baby cries is an important part of baby monitoring and health care. Almost all existing methods use supervised SVM, CNN, or their varieties. In this work, we propose to use weakly supervised anomaly detection to detect a baby cry. In this weak supervision, we only need weak annotation if there is a cry in an audio file. We design a data mining technique using the pre-trained VGGish feature extractor and an anomaly detection network on long untrimmed audio files. The obtained datasets are used to train a simple CNN feature network for cry/non-cry classification. This CNN is then used as a feature extractor in an anomaly detection framework to achieve better cry detection performance.

cs.CV↗

arXiv2023

Interplay between moment-dependent and field-driven unidirectional magnetoresistance in CoFeB/InSb/CdTe heterostructures

Magnetoresistance effects are crucial for understanding the charge/spin transport as well as propelling the advancement of spintronic applications. Here we report the coexistence of magnetic moment-dependent (MD) and magnetic field-driven (FD) unidirectional magnetoresistance (UMR) effects in CoFeB/InSb/CdTe heterostructures. The strong spin-orbital coupling of InSb and the matched impedance at the CoFeB/InSb interface warrant a distinct MD-UMR effect at room temperature, while the interaction between the in-plane magnetic field and the Rashba effect at the InSb/CdTe interface induces the marked FD-UMR signal that dominates the high-field region. Moreover, owning to the different spin transport mechanisms, these two types of nonreciprocal charge transport show opposite polarities with respect to the magnetic field direction, which further enable an effective phase modulation of the angular-dependent magnetoresistance. Besides, the demonstrations of both the tunable UMR response and two-terminal spin-orbit torque-driven magnetization switching validate our CoFeB/InSb/CdTe system as a suitable integrated building block for multifunctional spintronic device design.

physics.app-ph↗